{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")#不显示警告信息\n",
    "\n",
    "#参数初始化\n",
    "discfile = 'arima_data.xls'\n",
    "data = pd.read_excel(discfile, index_col = u'日期')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>3188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              销量\n",
       "日期              \n",
       "2015-01-01  3023\n",
       "2015-01-02  3039\n",
       "2015-01-03  3056\n",
       "2015-01-04  3138\n",
       "2015-01-05  3188"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#时序图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "data.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m1eHQT3g4Zm35Ii+z6nD3DrnhmP/45jGPzqkhX+RlVh0tf6R/cHCEHXs9HLPWfJGXWXW0dOgPjqT5xzcHyWTdn1Nrlb7IK5sNnt1zmD9/9ic8u+cwWf9/bAa0ePdO1p33dWX8Ii8O/ohN6z5d9vt4JJBZcS19pG/NySOBzIpz6FvT8Uggs+Ic+tZ0xkcC5fNIILMch741nUqOBPIJYWs2ZZ/IlbQVuAD4q4i4q8DrS4H/BbQBx4GbgSywK3kA3B4RO8qtwayQ8ZFAt/373yTTtZpf/fitZc3j4xPC1ozKOtKXdBPQFhGbgfMkbSjQ7MPAlyPifcA+4P3kbpT+QET0Jg8Hvs2JSkz37BPC1ozK7d7pZeKm6I8AV0xuEBF/FBHfS552A/uBy4EbJP1A0lZJp/2lIelWSX2S+vr7+8ssz2z2fELYmlG5od8J7E2WDwGrizWUtBlYHhFPAc8A10TEZcA84PrJ7SNiS0T0RERPd3d3meWZzZ5PCFszKjf0B4GFyXJXsfeRtAL4KvCxZNULEfFGstwHFOoWMqsLlZ4awieFrR6UeyJ3O7kunaeAjcArkxtI6gC+BXwmIvYkq++T9AXgReBG4O4yP99szlXqhDD4pLDVj3KP9B8EPiLpy8C/AF6SNHkEzy3AJuAOSdsk3Qx8HrgPeA54MiIeLfPzzaqiUvf/9UlhqxdlHelHxICkXuBa4IsRsQ94flKbe4B7Cmx+cTmfadbIpjopvGnd8hpVZa2o7HH6EXGYiRE8ZjYF3y/A6oWvyDWrAt8vwOqFQ9+sCip5vwCPArLZaOn59M2qqRL3C/AoIJstH+mbNRCPArLZcuibNRBPDWGz5dA3ayCeGsJmy6Fv1kA8Cshmy6Fv1kA8Cshmy6N3zBqMRwHZbPhI36wFeRRQ63Lom7UgjwJqXe7eMWtBlZ4LKJsNnnvtCLsPHmf9ys6yp6C2uefQN2tB46OAXnr1ALS1M39ee9mjgHx+oLG4e8esBVVyFJDPDzQWh75Zi6rUDWJ8fqCxOPTNbFZ8lXBjceib2axU8iphXzA293wi18xmpVI3kPcJ4eoo+0hf0lZJT0q6s5Q2M9nOzBpLJc4P+IRwdSii9D+fJN0E/HxEfFTSvcDvRcTO6doAF023Xb4V694e13723pLrA3ju+ecAuGTjJUXbZLLB8dH0lO+z8+UXAdhwwTvKqqPS7+OaXFM9vk8l3qv/2AgHBkdPW9/d1cEZi+fPqrZGk5Loml9+R8w3P/6z2yOip9Br5Yb+HwJ/ExF/LemDwMKI+O/TtQHeOYPtbgVuBeh6y09dev3n7iu5vpmaSeibWXUcG06z98gQ+ZEkwdplC1m8oLwAbNSd41yGfrnv2gnsTZYPAZtm2Gba7SJiC7AFoKenJ/73bZvLLHF6A8NjvLR3YM7e38xmrtJ9+tlscNvDW8l0reaGi943q6uEP3n/ZwD4T7/1UFnbl/peHe3i0nUryv6Mb368+Gvlhv4guSN3gC4Knxso1GYm25lZCxo/IVyJ6RzGdyCDF9wIbe384WM7fVI4UW7obgeuSJY3Artn2GYm25lZi0qlxKZ1y7lp01mzumBs/KQw7R2glE8K5yn3SP9B4AlJa4DrgA9Kuisi7pyizeVAFFhnZlZRU10lvGnd8hpVVR/KOtKPiAGgF3gKuCoinp8U+IXaHC20rvzSzcwKq+RVwtlsMLryrQyte3dTXDBW9unhiDgMfLPUNjPZzsxsNsavEp58UrjUq4Sb8dyAr8g1s6ZTqZPCp5wb4NQLxhq1m8ihb2ZNafyk8GzCuRnPDXjIpJlZEZWeQbQezg849M3Mihg/NzC/PYVg1jOIjp8fGDr3Sv7wsZ3c/d0fVj343b1jZlZEJS8Yq5fzAw59M7MpVOLcANTP+QF375iZVUG93GHMoW9mVgWVPD8wG+7eMTOrgkqeH5gNh76ZWZVU6vzArGqo2SebmVnVOfTNzFqIQ9/MrIU49M3MWohD38yshTj0zcxaiEPfzKyFOPTNzFqIQ9/MrIWUHPqStkp6UtKdU7RZKum7kh6R9BeSOiS1S3pV0rbkcdHsSjczs1KVFPqSbgLaImIzcJ6kDUWafhj4ckS8D9gHvB+4GHggInqTx47ZFG5mZqUr9Ui/F/hmsvwIcEWhRhHxRxHxveRpN7AfuBy4QdIPkr8WCs77I+lWSX2S+vr7+0ssz8zMpjJl6Ev6el53zDbgdmBv8vIhYPU0228GlkfEU8AzwDURcRkwD7i+0DYRsSUieiKip7u7u7TfxszMpjTlLJsRcVv+c0lfARYmT7uYYqchaQXwVeAXklUvRMRIstwHFOsaMjOzOVJq9852Jrp0NgK7CzWS1AF8C/hMROxJVt8naaOkNuBG4PnSyzUzs9kodT79B4EnJK0BrgMul3QB8KGIyB/NcwuwCbhD0h3APcDngfsBAQ9FxKOzrt7MzEpSUuhHxICkXuBa4IsRcRQ4Ctw5qd095IJ+sovLrNPMzCqg5DtnRcRhJkbwmJlZA2npK3LbU6LKt6c0M6uplg79RR3tXLBmCR3tLf01mFkLafm0W7xgHheftZSlC+fVuhQzsznX8qEPMK8txdvfspi1yxZO39jMrIE59BOSOGflIs4/czHtbe7oN7Pm5NCfZEVnBxetXUrn/LZal2JmVnEO/QIWzGvjwjVL6V7cUetSzMwqyqFfRFtKvHXVYs7r7vSwTjNrGg79aaxessDDOs2saTjJZsDDOs2sWTj0Z8jDOs2sGTj0S+BhnWbW6Bz6ZfCwTjNrVA79Mi2Y18Y7PKzTzBqMQ38WUh7WaWYNxqFfAR7WaWaNwilVIR7WaWaNwKFfQR7WaWb1ruTQl7RV0pOS7pyiTbukVyVtSx4XJet/R9Izkr42m6LrmYd1mlk9Kyn0Jd0EtEXEZuA8SRuKNL0YeCAiepPHDkmXAlcAlwH7JV0zq8rrnId1mlk9KvVIv5eJm6I/Qi7EC7kcuEHSD5K/DNqB9wDfiYgAHgauLLShpFsl9Unq6+/vL7G8+uJhnWZWb6YMfUlfz+ui2QbcDuxNXj4ErC6y6TPANRFxGTAPuB7onMm2EbElInoioqe7u7ukX6YeeVinmdWT9qlejIjb8p9L+gowfpayi+I7jRciYiRZ7gM2AIMz3LYprV6ygK757fxT/yDHRzK1LsfMWlSpwbudiS6djcDuIu3uk7RRUhtwI/B8Cds2rc757Vy0dinnrFzko34zq4kpj/QLeBB4QtIa4DrgckkXAB+KiPzRPJ8H7gcEPBQRj0pKAb+X/LXw/uTRciSxdtlCVnZ2sKv/OEeHxmpdkpm1EOXOq5awgbQcuBZ4PCL2lbjtQuADwLMRsWu69j09PdHX11dSfY1m/8Awew6dIJ0p7f8HM2teHe3i0nUryt5e0vaI6Cn0WqlH+kTEYSZG8JS67RDw7XK2bVarlixg2aIOdh88zsHB0VqXY2ZNrqVOptarjvYUP716Meefudjz95jZnCr5SN/mzorODpYunMerh06w7+hwrcsxsybkw8o605YS557RyYVrl7Cww1fzmlllOfTr1JIF87h47VLOWr7QwzvNrGIc+nUslRJnr1jERWctZfEC98SZ2ew59BvAoo52LlyzhHPP6KTNh/1mNgsO/QYhiTOXLuDis5ayvNM3ajGz8rjPoMEsmNfG285cwuBImv0DwxwYHCWT9YVdZjYzDv0G1TW/na7uLtatDA4OjvDmwAiDI+lal2Vmdc6h3+DaUmLVkgWsWrKA4yNp9h8b4cDgiKd1MLOCHPpNpHN+O+fOb+ecFYs4eHyE/QMjHBv20b+ZTXDoN6G2lFi1eAGrFi/gxGiaNwd89G9mOQ79Jreoo51zz2hn3YpFHPDRv1nLc+i3iNSko//9ydH/mI/+zVqKQ78FLepoZ/0Zub7/wydGGRhOMzA0xolR38bRrNk59FtYKiVWds1nZdd8AMYyWQaGxrwTMGtiDn07aV5byjsBsybn0Leiiu0Ejg2nGRge4/iIdwJmjabk0Je0FbgA+KuIuKtIm08ANydPlwFPA58EdiUPgNsjYkfJFVvNFNoJHEv+ChgYzv0lUOItl82sykoKfUk3AW0RsVnSvZI2RMTOye0i4h7gnmSbrwLfAC4GHoiIT1egbqsD89pSrOjsYEVnBwARwUg6y9BohqGxDMNjEz9H094bmNWDUo/0e5m4KfojwBXAaaE/TtJaYHVE9En6FeAGSVcBO4DbIuK0AeOSbgVuBTjnnHNKLM9qSRIL5rWxYF4byye9ls5kGU52CMOn7BCynjDOrIqmDH1JXwfOz1v1HmBrsnwI2DTN+3+S5IgfeAa4JiLekPSnwPXAQ5M3iIgtwBaAnp4ep0GTaG9L0dWWomv+6f/kRtIZhkezJ/8qGE5nGE1nGctkGcuEu4zMKmjK0I+I2/KfS/oKsDB52sUU8/FLSgFXAXckq16IiJFkuQ/YUE7B1nzmt7cxv72NpZx+n4CIYDQJ//EdwWg6m6zLeudgVqJSu3e2k+vSeQrYCLwyRdsrgacjTv6neJ+kLwAvAjcCd5f42daCJCU7BWB+8XbFdg7pbJDO5H5mssFYJtedlM56J2GtqdTQfxB4QtIa4DrgckkXAB+KiDsntf054PG8558H7gcEPBQRj5ZZs9lpZrpzyDe+M0hng0wmGMtmT9sxpDNBOlmf//BpCGtUJYV+RAxI6gWuBb4YEUeBo8DkwCciPjvp+YvkRvCY1YX2thTtbeVtG5HsACLIZiGdzZLNQibGdwq5nUY2m98uTr4eAdnI7TyyEUSynPFfIDbHSh6nHxGHmRjBY9aSJNHeprz/gMrcexQQeTuD3A5hYgeR2ynklk/+JEj+RyTP4+TziZ3I+HudXJ+0h1N3NHGyjvHncerzIusL/y5F1lN8o1rt9Er92ChQaLH3KP47FX6hPTV3ty/3FblmdUYSbYI2VOtSrAnN3e7EzMzqjkPfzKyFOPTNzFqIQ9/MrIU49M3MWohD38yshTj0zcxaiEPfzKyFOPTNzFqICl1KXC8k9QN7ZvEWZwAHKlRONbnu6nLd1eW65966iOgu9EJdh/5sSeqLiJ5a11Eq111drru6XHdtuXvHzKyFOPTNzFpIs4f+lloXUCbXXV2uu7pcdw01dZ++mZmdqtmP9M3MLI9D38yshTR86EvaKulJSafdp7eUNtUkaamk70p6RNJfSOoo0KZd0quStiWPi2pRazk1SfodSc9I+lq1ayxG0ify6n5O0tcLtKmr71zSaklPJMvzJP2lpO9L+tgU28yo3VyaVPc5yXf5mKQtkgreDkzSWkk/yfvuC44xn0uT6p5xPfWWL9Np6NCXdBPQFhGbgfMkbSinTQ18GPhyRLwP2Ae8v0Cbi4EHIqI3eeyoaoWFTVuTpEuBK4DLgP2Srql2kYVExD3jdQNPAH9coFndfOeSlgPfADqTVbcD2yPi3cAvSlpcZNOZtpsTBeq+DfhERFwNnA0U25G+C/hC3nffP/fVTihQ94zqqdN8mVJDhz7Qy8RN2h8hFzbltKmqiPijiPhe8rQb2F+g2eXADZJ+kBxJ1MP9jGdS03uA70RuhMDDwJVVrXAaktYCqyOir8DL9fSdZ4CbgYHkeS8T/44fB4pdJDTTdnPllLoj4o6I+GHy2kqKX9F6OfBvJT0r6e65L/M0k7/vmdbTS53ly3QaPfQ7gb3J8iFgdZltakLSZmB5RDxV4OVngGsi4jJgHnB9VYsrbCY11e33nfgkcE+R1+rmO4+IgYg4mrdqpt9rTb//AnUDIOlm4KWIeL3Ipt8lF6A/A2yWdPHcVXm6AnXPtJ56//d+mkYP/UFgYbLcReHfZyZtqk7SCuCrQLF+1xci4o1kuQ+ohz8bZ1JTXX7fAJJSwFXAtiJN6vE7HzfT77Xuvn9J5wGfAn59imZ/FxHHIiID/D21/+5nWk/dfd/TqfsCp7GdiT+nNgK7y2xTVcmJ228Bn4mIYhPK3Sdpo6Q24Ebg+aoVWNxMaqq77zvPlcDTUfzilHr8zsfN9Hutq+8/6St/APhYob8A8jws6S2SFgHvA16sSoGzr6euvu8ZiYiGfQBLyP2H+WXgh+S+9LumabO0Dur+BHCY3BHnNuBzBep+B1BUoicAAAC+SURBVPACsIPcCaV6+L5PqQlYAfzJpDYp4PvAV4BXgHNrXXdebXcDNyXLFzTId74t+bkOeCn5Xp8B2oCrgV+d1P60djWu+/eBN/L+rb+nSN1XAf+QfP+/Wu16C9R9Wj1F/s3UXb5M92j4K3KTI4lrgccjYl+5baxyJC0EPgA8GxG7al1Ps5C0htxR5cMxxVHzTNtZZTRavjR86JuZ2cw1ep++mZmVwKFvZtZCHPpmZi3EoW9m1kIc+mZmLeT/A4VMVSeJn7ofAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#自相关图\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plot_acf(data).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (1.813771015094526, 0.9983759421514264, 10, 26, {'1%': -3.7112123008648155, '5%': -2.981246804733728, '10%': -2.6300945562130176}, 299.4698986602418)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "#u+字符串表示后面字符串以 Unicode格式 进行编码，一般用在中文字符串前面，防止因为源码储存格式问题，导致再次使用时出现乱码。\n",
    "##返回值依次为adf、pvalue、usedlag、nobs、critical values、icbest、regresults、resstore\n",
    "print(u'原始序列的ADF检验结果为：', ADF(data[u'销量'])) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时序图显示该序列具有明显的单调递增趋势， 可以判断为是非平稳序列自相关图显示自相关系数长期大于零， 说明序列间具有很强的长期相关性；单位根检验统计量对应的p值显著大于0.05, 最终将该序列判断为非平稳序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#一阶 差分\n",
    "D_data = data.diff().dropna()\n",
    "D_data.columns = [u'销量差分']\n",
    "D_data.plot() #时序图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(D_data).show() #自相关图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "plot_pacf(D_data).show() #偏自相关图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "差分序列的ADF检验结果为： (-3.1560562366723537, 0.022673435440048798, 0, 35, {'1%': -3.6327426647230316, '5%': -2.9485102040816327, '10%': -2.6130173469387756}, 287.5909090780334)\n"
     ]
    }
   ],
   "source": [
    "print(u'差分序列的ADF检验结果为：', ADF(D_data[u'销量差分'])) #平稳性检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "差分序列的白噪声检验结果为： (array([11.30402222]), array([0.00077339]))\n"
     ]
    }
   ],
   "source": [
    "#白噪声检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "print(u'差分序列的白噪声检验结果为：', acorr_ljungbox(D_data, lags=1)) #返回统计量和p值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出的p值远小于0.05, 所以一阶差分之后的序列是平稳非白噪声序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:492: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:492: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:492: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:492: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "\n",
    "data[u'销量'] = data[u'销量'].astype(float)\n",
    "#定阶\n",
    "pmax = int(len(D_data)/10) #一般阶数不超过length/10\n",
    "qmax = int(len(D_data)/10) #一般阶数不超过length/10\n",
    "bic_matrix = [] #bic矩阵\n",
    "for p in range(pmax+1):\n",
    "  tmp = []\n",
    "  for q in range(qmax+1):\n",
    "    try: #存在部分报错，所以用try来跳过报错。\n",
    "      tmp.append(ARIMA(data, (p,1,q)).fit().bic)\n",
    "    except:\n",
    "      tmp.append(None)\n",
    "  bic_matrix.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            0           1           2           3\n",
      "0  432.068472  422.510082  426.088911  426.595507\n",
      "1  423.628276  426.073601         NaN         NaN\n",
      "2  426.774824  427.395856         NaN         NaN\n",
      "3  430.317524  431.924894  434.761700  436.478109\n"
     ]
    }
   ],
   "source": [
    "bic_matrix = pd.DataFrame(bic_matrix) #从中可以找出最小值\n",
    "print(bic_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC最小的p值和q值为：0、1\n"
     ]
    }
   ],
   "source": [
    "p,q = bic_matrix.stack().idxmin() #先用stack展平，然后用idxmin找出最小值位置。\n",
    "print(u'BIC最小的p值和q值为：%s、%s' %(p,q)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\Users\\Smile\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "model = ARIMA(data, (p,1,q)).fit() #建立ARIMA(0, 1, 1)模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>ARIMA</td>              <td>BIC:</td>          <td>422.5101</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>       <td>D.销量</td>          <td>Log-Likelihood:</td>     <td>-205.88</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-01-03 16:36</td>        <td>Scale:</td>          <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>         <td>36</td>               <td>Method:</td>         <td>css-mle</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>2</td>               <td>Sample:</td>       <td>01-02-2015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>           <td>34</td>                  <td></td>           <td>02-06-2015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>1.0000</td>      <td>S.D. of innovations:</td>   <td>73.086</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>No. Iterations:</td>        <td>15.0000</td>             <td>HQIC:</td>          <td>419.418</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>AIC:</td>             <td>417.7595</td>               <td></td>                <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>       <th>Coef.</th>  <th>Std.Err.</th>    <th>t</th>    <th>P>|t|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>      <td>49.9564</td>  <td>20.1390</td> <td>2.4806</td> <td>0.0182</td> <td>10.4847</td> <td>89.4281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.销量</th> <td>0.6710</td>   <td>0.1648</td>  <td>4.0712</td> <td>0.0003</td> <td>0.3480</td>  <td>0.9941</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>    <th>Real</th>   <th>Imaginary</th> <th>Modulus</th> <th>Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>-1.4902</td>  <td>0.0000</td>   <td>1.4902</td>   <td>0.5000</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                           Results: ARIMA\n",
       "====================================================================\n",
       "Model:              ARIMA            BIC:                 422.5101  \n",
       "Dependent Variable: D.销量             Log-Likelihood:      -205.88   \n",
       "Date:               2021-01-03 16:36 Scale:               1.0000    \n",
       "No. Observations:   36               Method:              css-mle   \n",
       "Df Model:           2                Sample:              01-02-2015\n",
       "Df Residuals:       34                                    02-06-2015\n",
       "Converged:          1.0000           S.D. of innovations: 73.086    \n",
       "No. Iterations:     15.0000          HQIC:                419.418   \n",
       "AIC:                417.7595                                        \n",
       "----------------------------------------------------------------------\n",
       "               Coef.    Std.Err.     t      P>|t|     [0.025    0.975]\n",
       "----------------------------------------------------------------------\n",
       "const         49.9564    20.1390   2.4806   0.0182   10.4847   89.4281\n",
       "ma.L1.D.销量     0.6710     0.1648   4.0712   0.0003    0.3480    0.9941\n",
       "-----------------------------------------------------------------------------\n",
       "                 Real           Imaginary          Modulus          Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "MA.1           -1.4902             0.0000           1.4902             0.5000\n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary2() #给出一份模型报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4873.9667493 , 4923.92317644, 4973.87960359, 5023.83603073,\n",
       "        5073.79245787]),\n",
       " array([ 73.08574293, 142.32679918, 187.542821  , 223.80281869,\n",
       "        254.95704265]),\n",
       " array([[4730.72132537, 5017.21217324],\n",
       "        [4644.96777602, 5202.87857687],\n",
       "        [4606.30242887, 5341.4567783 ],\n",
       "        [4585.19056646, 5462.48149499],\n",
       "        [4574.08583666, 5573.49907907]]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.forecast(5) #作为期5天的预测，返回预测结果、标准误差、置信区间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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